Differential Dissociation and Melting Curve Peak Detection

ABSTRACT

Systems and methods are provided for processing a melting or dissociation curve of a DNA or other sample, for example, during PCR processing. In some embodiments, detection of the melting point and melting curve behavior can be enhanced by taking a derivative of the curve, and detecting peaks in the differential dissociation curve. In some embodiments, the derivative operation can comprise the use of edge-processing, or other detection algorithms. In some embodiments, the dissociation analysis can comprise removing low-frequency (or pedestal) components of the differential dissociation curve. In some embodiments, the differential dissociation curve can exhibit a smoothed or more regular appearance than the raw detected data.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 60/898,310 filed Jan. 30, 2007, entitled “Differential Dissociationand Melting Curve Peak Detection,” and to U.S. Provisional PatentApplication No. 61/023,674 filed Jan. 25, 2008, to Francis T. CHENG etal. entitled “Methods of Dissociation Melt Curve, Analysis andCalibration,” both of which are incorporated by reference herein intheir entireties.

BACKGROUND

DNA amplification methods provide a powerful and widely used tool forgenomic analysis. Polymerase chain reaction (PCR) methods, for example,permit quantitative analysis to determine DNA copy number, sample sourcequantitation, and transcription analysis of gene expression. DNAanalysis methods allow the detection of single base changes in specificregions of the genome, such as single nucleotide polymorphisms (SNPs).SNP analysis and other techniques facilitate the identification ofmutations associated with specific diseases and conditions, such asvarious cancers, thalassemia, or others.

Many applications of PCR require the accurate generation of desiredamplification products versus the production of undesired artifacts. Oneuseful approach for validating the integrity of PCR reactions relies onmelting curve analysis to discriminate artifact from real amplificationproduct. Melting curve analysis can also be used to differentiate thevarious products of multiplexed DNA amplification, and to extend thedynamic range of quantitative PCR. DNA melting curve analysis can alsobe a powerful tool for optimizing PCR thermal cycling conditions, sincethe point at which DNA fragments or other material melts and separatecan be more accurately pinpointed.

One known approach for DNA melting curve analysis utilizes fluorescencemonitoring with intercalating double-strand-DNA specific dyes, such asfor example, SYBR Green. The SYBR Green dye attaches to the DNA asdouble-stranded DNA amplification products are formed, and continues tobind to the DNA as long as the DNA remains double-stranded. When meltingtemperatures are reached, the denaturation or melting of thedouble-stranded DNA is indicated and can be observed by a significantreduction in fluorescence, as SYBR Green dissociates from the meltedstrand. The detected dye fluorescence intensity typically decreasesabout 1000-fold during the melting process. Plotting fluorescence as afunction of temperature as the sample heats through the dissociationtemperature produces a DNA melting curve. The shape and position of theDNA melting curve is a function of the DNA sequence, length, and GC/ATcontent.

Currently known dissociation/melting curve analysis methods calculateand display the first derivative of multi-component dye intensity dataversus temperature, i.e., the differential melting curve. Thetemperature, T_(m), at a peak of the differential melting curvecharacterizes the product of the biochemical reaction. A sample withmultiple amplification products will show a melt curve with multiplepeaks in the differential melt curve. See generally, for example, FIG. 1(illustrating a single sample) and FIGS. 2(A) and 2(B) (illustratingmultiple samples).

Typically, during melting curve analysis, the raw data fluorescencemeasurements are taken at uneven or irregular temperature intervals.This can introduce undesired sensitivity to the sampling process alongthe temperature axis. Conventional signal processing techniques such asfiltering, differentiation, and the like, do not apply for data samplesat uneven temperature intervals. There is a need for techniques thatcorrect for uneven or irregular temperature interval sampling, and otherproblems in the field.

For example, calculating the differential dissociation curve can be anoisy process. The melt curve is inherently noisy, due, for example, tosampling or quantization errors, and traditional computationaldifferentiation methods can make noise issues worse. There is a need fortechniques that distinguish a genuine signal peak versus a noisy spike,and for techniques that distinguish a sample producing credible meltingcurve results, versus a sample producing unintelligible data.

Current dissociation curve analysis methods, moreover, typically assumea single peak in a differential dissociation curve. There is a need formelting curve analysis methods for gene expression and other purposesthat can detect multiple peaks of a differential melting curve. There isa further need for melting curve techniques that can be applied to, orimplemented in, automated validation techniques, among otherapplications.

SUMMARY

According to various embodiments of the present teachings, systems andmethods are provided which receive and analyze fluorescent or otheremission data generated by samples in PCR or other processes astemperatures vary during melting or dissociation phenomena. According tovarious embodiments, the fluorescent emission spectra of one or more DNAor other samples can be captured or received as a function oftemperature or other parameters, and the raw dissociation curve plotted.According to various embodiments, the analysis can includeinterpolating, over sampling, or resampling the source or rawdissociation curve to produce a curve or representation havingequally-spaced temperature intervals. According to various embodiments,the analysis can comprise inspecting the spectral plot of the rawdissociation curve to identify curves containing comparatively largeamounts of power in upper frequencies, indicating extraneous noise inthe detection process. According to various embodiments, dissociationcurves whose normalized variance exceeds a predetermined threshold canbe discarded as unreliable. According to various embodiments, theanalysis can further comprise removing low-frequency components of theraw dissociation curve, to better isolate melting behavior. According tovarious embodiments, the raw dissociation curve can be subjected to aderivative computation, for example a first derivative, to assist inidentifying points of sharpest change in the dissociation data in turnindicating a possible melting point. According to various embodiments,the derivative computation can comprise the application of an edgefilter, for example a Canny filter or other filter or computation.According to various embodiments, the dissociation curve afterderivative processing can contain multiple identified melting points.

FIGURES

FIG. 1 illustrates a dissociation/melting curve, according to variousembodiments of the present teachings.

FIGS. 2(A) and 2(B) illustrate a set of melt curves and differentialmelt curves having multiple products, according to various embodimentsof the present teachings.

FIG. 3 illustrates a flow diagram of interpolation processing, accordingto various embodiments of the present teachings.

FIG. 4(A) illustrates a melt curve and a set of related derivative andpower spectrum curves reflecting noisy sample data, according to variousembodiments of the present teachings.

FIG. 4(B) illustrates a melt curve and a set of related derivative andpower spectrum curves reflecting good or reliable sample data, accordingto various embodiments of the present teachings.

FIG. 4(C) illustrates a melt curve and a set of related derivative andpower spectrum curves reflecting marginal sample data, according tovarious embodiments of the present teachings.

FIG. 5(A) illustrates the generation of a set of differential meltcurves, according to various embodiments of the present teachings.

FIG. 5(B) illustrates the generation of a set of differential meltcurves, according to various embodiments of the present teachings inanother regard.

FIG. 6 illustrates a flow diagram of a pedestal removal algorithm,according to various embodiments of the present teachings.

DESCRIPTION

According to various embodiments of the present teachings, systems andmethods are provided that operate on raw dissociation data plots togenerate a first-order or other derivative plot of the original emissiondata. According to various embodiments, the emission data can comprise agraph, chart, or other representation of the dye emission of one or morefluorescently-labeled samples, such as DNA samples, as a function oftemperature. According to various embodiments, the raw emission data ofthe dissociation/melting curve or other data can be pre-processed orotherwise conditioned to improve the downstream analysis. According tovarious embodiments, for example, the analysis can compriseinterpolating the measurement data taken at unevenly-spaced temperatureintervals into data samples at equally-spaced temperature intervals.According to various embodiments, an equal spacing interpolation, orother resampling or oversampling step, can improve the mathematicalintegrity or capability of the subsequent calculations, including, forexample, to permit Fourier or other frequency-domain transformations.According to various embodiments, the original raw or source data cancomprise data samples at irregular temperature intervals, since the rateof change in temperature can vary at different points in the PCR orother cycle or process. According to various embodiments, resampling,oversampling, interpolating, or otherwise processing the fluorescentsignal-versus-temperature graph to produce data points at equally-spacedtemperature intervals can provide modified data which is capable ofbeing subjected to frequency domain analysis. In some embodiments, rawdissociation data that is interpolated, oversampled, or resampled toproduce data points at equally-spaced temperature intervals can besubjected to a Fourier transform, to develop a frequency-domain orspectral representation of the original melting curve, or of processedmelting curves derived from the original melting curve. The frequencytransform or operator can comprise a discrete-time Fourier transform, acontinuous Fourier transform, a Fast Fourier Transform, a wavelettransform, or other transform, algorithm, or operator.

According to various embodiments, interpolation processing to produceequally-spaced data points along the temperature axis can compriseprocessing algorithms shown in the flow diagram of FIG. 3. In step 302,processing can begin. In step 304, a nominal temperature increment orstep (dT) can be determined, for example, by dividing the totaltemperature range by the number of data points. In step 306, a newtemperature axis or list of temperatures can be generated having double,or another multiple, of the number of original data points. In step 308,one temperature data point (Ti) can be taken from the list. In step 310,the measured data points can be marked with a user-specified or otherneighborhood or region of, around, or in proximity to Ti. In step 312,each marked data point can be weighed or adjusted by a window weightingfunction based on its distance from Ti. In step 314, a weighted sum ofall marked data points can be generated or calculated as the new datasample value. In step 316, a determination can be made whether the endof the emission data list has been reached. If the end of the list hasbeen reached, processing can terminate in step 318. If the end of thelist has not been reached, processing can return to step 308, repeat, orother action can be taken. According to various embodiments as shown inFIG. 3, the resulting interpolation can produce a data sequence withmore data samples than the original data sequence. For example,according to embodiments, the interpolated data sequence can comprisetwice the data samples of the original data sequence. In someembodiments, the interpolated data sequence can comprise another integeror non-integer multiple of the original number of samples or datapoints, or another number of output samples.

According to various embodiments, further processing or dataconditioning can be performed on the raw or interpolated dissociationcurve or related data. For example, the dissociation analysis cancomprise steps that detect and identify noisy data samples, to eliminatethe effects of those sources on further analysis. Illustrations ofdissociation curves exhibiting different good, marginal, and noisydetected patterns of melt curve behavior are shown, for example, inFIGS. 4(A)-4(C). Noisy data samples can corrupt further PCR or otheranalysis. The analysis can therefore in one regard reject, remove, orexclude emission data from samples identified as noisy samples fromfurther analysis. According to various embodiments, the noisy datasamples can be normalized or otherwise processed for incorporation infurther calculations. According to various embodiments, the detection ofnoisy data samples can comprise applying spectral domain analysis anddetection techniques to the raw or interpolated data. According tovarious embodiments, the dissociation analysis and processing cancomprise, for example, calculating a power spectrum of the interpolatedmelting curve.

Computed power spectra of a noisy, good, and marginal well or sample areshown in the upper-right graph of FIGS. 4(A), 4(B), and 4(C),respectively. In general, a noisy well or sample will tend to show asignificant amount of power present in the upper frequency ranges,indicating the random, spurious, or rapid spikes or transitionsassociated with noise content. According to various embodiments, thedissociation analysis can, for example, comprise setting thedissociation curve data sampling rate at about 1 Hz, and calculating anormalized variance of the power spectrum of the dissociation curve fromabout ¼ Hz to about ½ Hz. Other sampling rates can be used.

According to various embodiments, the power spectrum of an interpolatedwell or sample series can be quantitatively processed to identify noisywells or samples. For example, a normalized variance of the powerspectrum curve of the sample series can be computed. In someembodiments, if the normalized variance of the dissociation curve isabove a defined noise discrimination threshold, the sample data can beclassified as noise. According to various embodiments, the noisediscrimination threshold can comprise a user-defined threshold.According to various embodiments, the noise discrimination threshold cancomprise an automatically-generated threshold, for instance based onstatistical measures. According to various embodiments, the noisediscrimination threshold can comprise an empirically-derived threshold,for instance, an average threshold of known good wells or samples. Insome embodiments, the normalized, rather than absolute, variance orother statistical measure can be used to accommodate data from differentsamples, for example, to process samples displaying different initialfluorescent intensities.

According to various embodiments, the analysis can comprise filteringthe interpolated temperature data by a Gaussian kernel or otherfunction. According to various embodiments, the filtered, interpolateddata can be further filtered or processed by the derivative of theGaussian kernel, or other derivative or other function. According tovarious embodiments, application of a derivative function, for instancea first-order derivative function, can produce a differential melt ordissociation curve, such as, for example, the curves shown in FIG. 5(A).FIG. 5(A) displays differential traces for a set of multiple samples orwells. According to various embodiments, when a first-order derivativeis computed, the analysis can comprise utilizing a Canny edge detectionalgorithm filtering technique to calculate the first derivative of thedissociation curve. The Canny filtering technique is, for example,described in: J. Canny, “A Computational Approach to Edge Detection”,IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8,No. 6, Nov. 1986, which document is herein incorporated in its entiretyby reference. As, for example, illustrated in FIG. 5(B), edge detectionprocessing can reduce the effects of sharpened transitions in noisyderivative signals.

According to various embodiments, the dissociation analysis can furthercomprise extrapolating data points at the beginning and at the end ofthe raw or interpolated dissociation curve, before the first derivativecalculation. This can, for instance, improve the correctness or accuracyof the first derivative calculations at the beginning and at the end ofthe dissociation curve.

According to various embodiments, the dissociation analysis can comprisedetecting and analyzing the peaks of the first derivative of thedissociation curve (i.e., the differential melting curve), that sit ontop of a low-frequency “pedestal” or offset. According to variousembodiments, the “pedestal” can designate very low frequency componentsof the differential melting curve. According to various embodiments, theanalysis can comprise removing the pedestal or low-frequency components,and evaluating the heights of the modified differential melting curvepeaks left after the pedestal or baseline is subtracted or otherwisecompensated for. According to various embodiments, techniques forremoving the pedestal can comprise the processing shown in the flowdiagram illustrated in FIG. 6. In step 602, processing can begin. Instep 604, a data segment can be received, for instance, a fluorescentemission series from one or more samples obtained or captured from a PCRmachine, or other source. In step 606, the initial or left-most peak canbe identified, for example, the left-most peak labeled indexPeak. Instep 608, a first valley to the left of the initially identifiedindexPeak can be identified, pointed to by a parameter such as indexLeftwith height leftHeight. In step 610, a first valley to the right of theinitially identified indexPeak can be identified, pointed to by aparameter such as indexRight with height rightHeight. In step 612, avariable pedestalHeight can be set to the maximum of the parametersleftheight and rightHeight. In step 614, the data segment to the left ofindexRight can be thresholded by applying pedestalHeight as a thresholdvalue, thus generating the first peak with the pedestal or low-frequencybaseline removed. In step 616, data to the left of indexRight of thecurrent data segment can be recursively removed, to thereby subtract orequalize for the pedestal throughout the source data set. In step 618,processing can end, repeat, return to a prior processing point, orproceed to a further processing point.

According to various embodiments, the dissociation analysis can compriseranking the detected, pedestal-removed peaks by their relative heightswith respect to the tallest peak. According to various embodiments, theuser can specify a fractional score as the peak detection threshold, andthe analysis can comprise reporting those peaks that have a relativeheight above that reporting threshold. For example, the tallest peak canbe given a fractional score of 100. If a fractional score peak detectionthreshold is set at 40, then only peaks above 40% of the tallest peakwill be reported, and the lower height peaks will be regarded as noise.According to various embodiments, the peaks falling below the peakdetection threshold can be removed or discarded. According to variousembodiments, the peak detection threshold can be automatically computed,for example based on standard deviation measures on the peaks, or othermetrics or measures. According to various embodiments, any of the rawdetection data, normalized differential melting curves, or other data,charts, graphs, or information can be stored to, and/or displayed orpresented to a user by, a computer, instrument, or other hardware ordevice.

According to various embodiments, the dissociation or melting curveanalysis can take place during, or subsequent to, amplification, or inthe absence of amplification. Furthermore, while various embodimentsherein are described in connection with PCR, according to variousembodiments, other methods of amplification can be compatible withdifferential dissociation or melting curve analysis according to thepresent teachings. Moreover, while reference is made to amplification,according to various embodiments, the differential dissociation/meltingcurve analysis of the present teachings can be performed on nucleic acidsamples that have been obtained without amplification, or can be appliedto other processes or chemistries. Furthermore, while description ismade herein of analyzing DNA or fragments of DNA to determine meltingpoints and other data, according to various embodiments, chemicals,substances, samples, or materials can be analyzed according to thepresent teachings.

According to various embodiments, different aspects of the differentialdissociation/melting curve analysis of the present teachings can beapplied to commercial systems and implementations, such as the Step One™machine commercially available from Applied Biosystems, Foster City,Calif., and described, for example, a publication entitled “AppliedBiosystems Step One Real-Time PCR System Getting Started Guide,” whichpublication is incorporated by reference in its entirety herein.

The differential dissociation/melting curve analysis according tovarious embodiments of the present teachings can be utilized inautomated systems and techniques such as those described, for example,in the publication, by Mann et al., entitled “Automated Validation ofPolymerase Chain Reactions Using Amplicon Melting Curves,” Proceedingsof the Computational Systems Bioinformatics Conference, Aug. 8-11, 2005,Stanford, Calif., pp. 377-385, which publication is incorporated byreference in its entirety herein.

Various embodiments of the present teachings can be implemented, inwhole or part, in digital electronic circuitry, or in computer hardware,firmware, software, or in combinations thereof. Apparatus of theinvention can be implemented in a computer program, software, code, oralgorithm embodied in machine-readable media, such as electronic memory,CD-ROM or DVD discs, hard drives, or other storage device or media, forexecution by a programmable processor. Various method steps according tothe present teachings can be performed by a programmable processorexecuting a program of instructions to perform functions and processesaccording to the present teachings, by operating on input data andgenerating output. The present teachings can, for example, beimplemented in one or more computer programs that are executable on aprogrammable system including at least one programmable processorcoupled to receive data and instructions from, and to transmit data andinstructions to, a data storage system or memory, at least one inputdevice such as a keyboard and mouse, and at least one output device,such as, for example, a display or printer. Each computer program,algorithm, software, or code can be implemented in a high-levelprocedural or object-oriented programming language, or in assembly,machine, or other low-level language if desired. According to variousembodiments, the code or language can be a compiled, interpreted, orotherwise processed for execution.

Various processes, methods, techniques, and algorithms can be executedon processors that can include, by way of example, both general andspecial purpose microprocessors, such as, for example, general-purposemicroprocessors such as those manufactured by Intel Corp. or AMD Inc.,digital signal processors, programmable controllers, or other processorsor devices. According to various embodiments, generally a processor willreceive instructions and data from a read-only memory and/or a randomaccess memory. According to various embodiments, a computer implementingone or more aspects of the present teachings can generally include oneor more mass storage devices for storing data files, such as magneticdisks, such as internal hard disks and removable disks, magneto-opticaldisks, and CD-ROM DVD, Blu-Ray, or other optical disks or media. Memoryor storage devices suitable for storing, encoding, or embodying computerprogram instructions or software and data can include, for instance, allforms of volatile and non-volatile memory, including for examplesemiconductor memory devices, such as random access memory,electronically programmable memory (EPROM), electronically erasableprogrammable memory, EEPROM, and flash memory devices, as well asmagnetic disks such as internal hard disks and removable disks,magneto-optical disks, and optical disks. Any of the foregoing can besupplemented by, or incorporated in, ASICs. According to variousembodiments, processors, workstations, personal computers, storagearrays, servers, and other computer, information, or communicationresources used to implement features of the present teachings can benetworked or network-accessible.

Other embodiments will be apparent to those skilled in the art fromconsideration of the present specification and practice of the presentteachings disclosed herein. For example, resources described in variousembodiments as singular can, in embodiments, be implemented as multipleor distributed, and resources described in various embodiments asdistributed can be combined. It is intended that the presentspecification and examples be considered as exemplary only.

1. A method for determining the differential dissociation curve of atleast one sample, comprising: interpolating emission measurement data ofthe at least one sample taken at uneven temperature intervals into dataat equally-spaced temperature intervals; and generating a differentialdissociation curve by generating a derivative of the emissionmeasurement data.
 2. The method of claim 1, further comprising detectingat least one peak in the differential dissociation curve.
 3. The methodof claim 1, further comprising modifying the differential dissociationcurve.
 4. The method of claim 1, further comprising generating a powerspectrum of the interpolated emission measurement data.
 5. The method ofclaim 1, wherein the derivative comprises a first-order derivative. 6.The method of claim 1, further comprising performing a frequency-domaintransform on the interpolated emission measurement data.
 7. The methodof claim 1, wherein the at least one sample comprises a plurality ofsamples each having associated emission measurement data.
 8. A systemfor determining the differential dissociation curve of at least onesample, comprising: an input unit for receiving emission data of atleast one sample taken at uneven temperature intervals; and a processor,communicating with the input unit, the processor being configured tointerpolate the emission measurement data of the at least one sampletaken at uneven temperature intervals into data at equally-spacedtemperature intervals, and generate a differential dissociation curve bygenerating a derivative of the emission measurement data.
 9. The systemof claim 8, wherein the processor is further configured to detect atleast one peak in the differential dissociation curve.
 10. The system ofclaim 8, wherein the processor is further configured to modify thedifferential dissociation curve.
 11. The system of claim 10, wherein themodifying comprises removing emission measurement data associated withpeaks that fall below a peak detection threshold.
 12. A differentialdissociation curve generated for at least one sample, the differentialdissociation curve being generated by a method comprising: interpolatingemission measurement data of the at least one sample taken at uneventemperature intervals into data at equally-spaced temperature intervals;and generating a differential dissociation curve by generating aderivative of the emission measurement data.
 13. The differentialdissociation curve of claim 12, wherein the method further comprisesgenerating a power spectrum of the interpolated emission measurementdata.
 14. The differential dissociation curve of claim 13, whereingenerating a power spectrum comprises generating a normalized varianceof the power spectrum and removing the emission measurement data of theat least one sample when the normalized variance of the power spectrumexceeds a predetermined threshold.
 15. A computer-readable medium, thecomputer-readable medium being readable to execute a method fordetermining the differential dissociation curve of at least one sample,the method comprising: interpolating emission measurement data of the atleast one sample taken at uneven temperature intervals into data atequally-spaced temperature intervals; and generating a differentialdissociation curve by generating a derivative of the emissionmeasurement data.
 16. The computer-readable medium of claim 15, whereinthe method further comprises modifying the differential dissociationcurve.
 17. The computer-readable medium of claim 15, wherein the methodfurther comprises generating a power spectrum of the interpolatedemission measurement data.
 18. The computer-readable medium of claim 15,wherein the derivative comprises a first-order derivative.
 19. Thecomputer-readable medium of claim 15, wherein the method furthercomprises performing a frequency-domain transform on the interpolatedemission measurement data.
 20. The computer-readable medium of claim 15,wherein the differential dissociation curve is generated in connectionwith a polymerase chain reaction process.